In this paper, we propose a method for recognizing products of different sizes that are sold in convenience stores. The need for robots to operate products in stores instead of employing people has recently been increasing. Such robots are required to estimate the 6DoF poses of more than 2,000 products to display and dispose of them. Previous methods for 6DoF pose estimation use three-dimentional computer-aided design (3D-CAD) models of objects. However, these methods require high computational costs because models need to be prepared for each object. Our method uses object shapes, i.e., “cuboid”, “isosceles triangular prism”, “cylinder” and “regular triangular prism”. The method consists of two modules. First module that generates candidates for the shape and size of an object. Second module that extracts an optimal hypothesis from hypotheses. First, in the method, many hypotheses for various sizes and 6DoF poses are generated using the numbers of surfaces of each shape and the positional relationship among these surfaces. Second, the sizes and 6DoF poses of objects determined in the hypotheses are evaluated in depth images. Finally, the optimal hypothesis is determined using validation module. We conducted an evaluation experiment to evaluate the of proposed method by generating 100 objects of different sizes in a virtual space and applying this method to them. The recognition rate of isosceles triangular prisms was 84%, that of cuboids was 93%, and that of cylinders was 94%. Thus, the objects were recognized without the need of using 3D-CAD models.
In this paper, we propose a method for automatically determining the motion parameters of robots to execute target tasks such as “scooping powdered tea and putting it into a teacup”. For robots to handle everyday objects, it is necessary to determine the motion parameters of robots for handling objects of various shapes and sizes. There are two methods for determining motion parameters. One involves using a 3D model of an object and the other does not involve using such a model. The latter method is effective in places where there are a wide variety of everyday objects such as in homes. However, it is assumed with this method that an object is placed face up. Therefore, this method cannot be used when an object is placed face down. We propose a method for determining motion parameters for handling changes in the shapes, sizes and poses (face up or face down) of objects. Our method uses a 3D deep neural network to recognize an object’s functions (e.g., “scoop” and “grasp”) and recognizes the poses of an object from the function information. Motion parameters are then determined based on the recognition results. We conducted an experiment to evaluate the performance of the method by testing it on five spoons of different shapes, sizes, and poses. The method had a success rate of approximately 86%.